In the past, tool life tests have been performed using a conventional Taylor testing
technique. This methodology is expensive and time-consuming. It requires wearing a
number of tools until the tool failure criterion has been reached. A number of short-term
tests designed to replace the Taylor test have been proposed but they suffer from a
number of drawbacks. Many of these tests are performed under non—standard cutting
conditions or require special workpiece preparation or equipment. As a result, tool life
models developed from these tests are of limited usefulness in predicting tool failure times
for conventional machining operations.

A methodology is required which combines the time and cost advantages of non-conventional
tests with statistical validity and robustness. In this research, two short-term
tests are presented which are based on the Taylor test. Response surface models are used
to develop the parameters of Taylor's tool life equation. The tests are shortened by using
regression equations of flank wear data to predict the tool failure time without wearing the
tool to failure. The two methods, abbreviated conventional testing and sequential
composite testing, are statistically validated and compared with the Hill Taylor test. The
results show that these tests can accurately predict tool life and the resulting Taylor
models are not significantly different from those estimated by conventional means.